Creating an effective representation space is crucial for mitigating the
curse of dimensionality, enhancing model generalization, addressing data
sparsity, and leveraging classical models more effectively. Recent advancements
in automated feature engineering (AutoFE) have made significant progress in
addressing various challenges associated with representation learning, issues
such as heavy reliance on intensive labor and empirical experiences, lack of
explainable explicitness, and inflexible feature space reconstruction embedded
into downstream tasks. However, these approaches are constrained by: 1)
generation of potentially unintelligible and illogical reconstructed feature
spaces, stemming from the neglect of expert-level cognitive processes; 2) lack
of systematic exploration, which subsequently results in slower model
convergence for identification of optimal feature space. To address these, we
introduce an interaction-aware reinforced generation perspective. We redefine
feature space reconstruction as a nested process of creating meaningful
features and controlling feature set size through selection. We develop a
hierarchical reinforcement learning structure with cascading Markov Decision
Processes to automate feature and operation selection, as well as feature
crossing. By incorporating statistical measures, we reward agents based on the
interaction strength between selected features, resulting in intelligent and
efficient exploration of the feature space that emulates human decision-making.
Extensive experiments are conducted to validate our proposed approach.

通过引入一种交互感知的增强生成视角，我们重新定义特征空间重建为通过选择创建有意义的特征和控制特征集大小的嵌套过程，从而自动化特征和操作的选择以及特征交叉。通过结合统计度量，我们基于所选特征之间的相互作用强度奖励智能体，实现了对特征空间的智能和高效的探索，从而模拟人体决策过程。通过进行大量实验证实了我们提出的方法。